Machine Learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection

Chia, Zheng Lin; Ptaszyński, Michał; Masui, Fumito; Leliwa, Gniewosz; Wroczyński, Michał

doi:10.1016/j.ipm.2021.102600

Cited by 63 publications

(29 citation statements)

References 7 publications

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“…Although, these ensemble models reduced the training and execution time for classification, the major limitation comes when utilized sarcasm tweets and multiple-meaning acronym terms. Chia et al [8] also utilized different ML and feature engineeringbased approaches to classify irony and sarcasm from cyberbullying tweets. In this approach, many classifiers and feature selection methods were tested; while this approach greatly detects the sarcasm and irony terms among cyberbullying tweets, the detection rate is still very low [33].…”

Section: Related Workmentioning

confidence: 99%

“…Cyberbullying detection within the Twitter platform has largely been pursued through tweet classification and to a certain extent with topic modeling approaches. Text classification based on supervised machine learning (ML) models are commonly used for classifying tweets into bullying and non-bullying tweets [8] [9], [10], [11], [12], [13], [14], [15], [16],and [17]. Deep learning (DL) based classifiers have also been used for classifying tweets into bullying and non-bullying tweets [18], [19], [20], [21], [22], and [7].…”

Section: Introductionmentioning

confidence: 99%

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DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

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Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

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“…This research advances a novel DDT strategy that processes input components by utilizing the DNN hidden layer as tree nodes, as demonstrated in previous research. Chia et al [ 16 ] use feature engineering and machine learning approaches to explore the use of irony and sarcasm on social media platforms. To begin, they define and assess the definitions of sarcasm and irony by looking at a large number of research studies that are focused on the contexts in which they are used.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

Neelakandan¹,

Sridevi

Chandrasekaran

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the ease with which the internet and cell phones can be accessed, online social networks (OSN) and social media have seen a significant increase in popularity in recent years. Security and privacy, on the other hand, are the key concerns in online social networks and other social media platforms. On the other hand, cyberbullying (CB) is a serious problem that needs to be addressed on social media platforms. Known as cyberbullying (CB), it is defined as a repetitive, purposeful, and aggressive reaction performed by individuals through the use of information and communication technology (ICT) platforms such as social media platforms, the internet, and cell phones. It is made up of hate messages that are sent by e-mail, chat rooms, and social media platforms, which are accessed through computers and mobile phones. The detection and categorization of CB using deep learning (DL) models in social networks are, therefore, crucial in order to combat this trend. Feature subset selection with deep learning-based CB detection and categorization (FSSDL-CBDC) is a novel approach for social networks that combines deep learning with feature subset selection. The suggested FSSDL-CBDC technique consists of a number of phases, including preprocessing, feature selection, and classification, among others. Additionally, a binary coyote optimization (BCO)-based feature subset selection (BCO-FSS) technique is employed to select a subset of features that will increase classification performance by using the BCO algorithm. Additionally, the salp swarm algorithm (SSA) is used in conjunction with a deep belief network (DBN), which is known to as the SSA-DBN model, to detect and characterize cyberbullying in social media networks and other online environments. The development of the BCO-FSS and SSA-DBN models for the detection and classification of cyberbullying highlights the originality of the research. A large number of simulations were carried out to illustrate the superior classification performance of the proposed FSSDL-CBDC technique. The SSA-DBN model has exhibited superior accuracy to the other algorithms, with a 99.983 % accuracy rate. Overall, the experimental results revealed that the FSSDL-CBDC technique beats the other strategies in a number of different aspects.

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“…Later, (Eronen et al, 2021 ) used the re-annotated dataset version of (Ptaszynski et al, 2018 ) to study the effect of Feature Density using various feature preprocessing methods to estimate dataset complexity and, in consequence, to evaluate the performance of machine learning classifiers before starting to train the models. Chia et al ( 2021 ) conducted a study to show the practical applicability of sarcasm and irony detection in other NLP tasks, such as CB. They built an ML model trained on a dataset dedicated for sarcastic detection and tested it on the re-annotated version of the Formspring CB dataset using the same settings.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…However, not all forms of CB have the same harmful impact. For instance, even if it appears only once, a threatening message might be considered dangerous or even more than repeated irony messages (Chia et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The design, construction and evaluation of annotated Arabic cyberbullying corpus

2022

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Cyberbullying (CB) is classified as one of the severe misconducts on social media. Many CB detection systems have been developed for many natural languages to face this phenomenon. However, Arabic is one of the under-resourced languages suffering from the lack of quality datasets in many computational research areas. This paper discusses the design, construction, and evaluation of a multi-dialect, annotated Arabic Cyberbullying Corpus (ArCybC), a valuable resource for Arabic CB detection and motivation for future research directions in Arabic Natural Language Processing (NLP). The study describes the phases of ArCybC compilation. By way of illustration, it explores the corpus to discover strategies used in rendering Arabic CB tweets pulled from four Twitter groups, including gaming, sports, news, and celebrities. Based on thorough analysis, we discovered that these groups were the most susceptible to harassment and cyberbullying. The collected tweets were filtered based on a compiled harassment lexicon, which contains a list of multi-dialectical profane words in Arabic compiled from four categories: sexual, racial, physical appearance, and intelligence. To annotate ArCybC, we asked five annotators to classify 4,505 tweets into two classes manually: Offensive/non-Offensive and CB/non-CB. We conducted a rigorous comparison of different machine learning approaches applied on ArCybC to detect Arabic CB using two language models: bag-of-words (BoW) and word embedding. The experiments showed that Support Vector Machine (SVM) with word embedding achieved an accuracy rate of 86.3% and an F1-score rate of 85%. The main challenges encountered during the ArCybC construction were the scarcity of freely available Arabic CB texts and the deficiency of annotating the texts.

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Machine Learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection

Cited by 63 publications

References 7 publications

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

The design, construction and evaluation of annotated Arabic cyberbullying corpus

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